AGRICAM: A Track-Mounted Crop Pollination Monitoring Robot

Malika Nisal Ratnayake; Adel N. Toosi; James Cook; Romina Rader; Alan Dorin

cs.CVAug 29, 2026

AGRICAM: A Track-Mounted Crop Pollination Monitoring Robot

Authors: Malika Nisal Ratnayake, Adel N. Toosi, James Cook, Romina Rader, Alan Dorin

Organizations: Department of Data Science and Artificial Intelligence, Monash University, Wellington Road, Clayton, VIC, 3800, Australia · School of Computing and Information Systems, The University of Melbourne, Parkville, Melbourne, VIC, 3052, Australia · Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia · School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia

Abstract

Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances in computer vision and deep learning have enabled detailed analysis of pollinator behaviour, but monitoring must trade-off detail against spatial coverage and human or technological resources. This paper presents the Automated Guided Robot for Insect and Crop Activity Monitoring (AGRICAM), a purpose-built robotic system designed to meet the requirements of large-scale pollination monitoring in protected cropping systems. AGRICAM operates autonomously on low-cost, easily installed track for movement along crop rows, without disrupting farm operations or insect behaviour. The platform integrates two RGB cameras, microclimate sensors, GPS and RFID modules, motion sensors, and 4G cellular network connectivity for data transmission. A web interface enables remote device configuration and scheduling. The system autonomously captures video and image data of insects' locations and local environmental conditions. These are transferred to the cloud and analysed using computer vision models to quantify pollinator visitation and spatio-temporal activity variation. We deployed the system on a commercial blueberry farm to demonstrate and test its capability. It successfully mapped insect pollination patterns across 80 m long industrial polytunnels over 30 hours. This data enabled spatial analyses of insect activity we used to confirm a uniform pollinator distribution within polytunnels, as desired by the farm management team. The data also highlighted variation of insect activity associated with time of day and microclimate. AGRICAM therefore has been shown to be a scalable, automated crop pollination monitor that can support data-driven decisions to enhance pollination management, thereby improving crop productivity and food security.

Figures & tables

Figure 1: Overview of the AGRICAM processing pipeline. Video, location, and microclimate data are collected in the field using AGRICAM, transferred via cloud (or USB storage if desired), and processed by AI-facilitated workflows to automatically detect and track insect pollinators. The data are subsequently analysed and visualisations of spatiotemporal patterns of microclimate and insect behaviour are prepared.

Panel of Figure 2 — Figure 2: AGRICAM system components . (a) System installation overview, (b) components of the AGRICAM robot, and (c) deployed system in a commercial blueberry farm. In (a) and (c), the AGRICAM unit is mounted on a PVC track along the leg rows of a protected cropping structure (polytunnel). Each monitoring station is identified by an RFID tag and positioned at structurally supported locations to minimise stress on the PVC track. In (b), task-specific components of the AGRICAM system are shown in different colours.

Panel of Figure 3 — Figure 3: AGRICAM web user interface. (a) System status dashboard, (b) monitoring path map, (c) control and diagnostics panel, and (d) configuration menu.

Panel of Figure 4 — Figure 4: AGRICAM field test site detail. (a) A map of the study site at Costa Berries Exchange, New South Wales, Australia. The location of managed beehives is indicated by a yellow pentagon. The crop rows where AGRICAM was deployed are labelled Row A and Row B, and the length of each monitored tunnel is indicated. Arrows show the direction the AGRICAM camera faces during row traversal. (b) Schematic representation of the tunnel configuration and blueberry row numbering, shown as viewed from the polytunnel end marked 0 m in (a). (c) Example image illustrating the field of view captured by AGRICAM.

Figure 5: Spatial and temporal distribution of video recordings during field deployment. Bubble plots show the number of videos recorded (colour scale 0–6) by distance from the polytunnel end (meters, x-axis) and by 2-hour time bin (09:00–, 11:00–, 13:00–, 15:00–16:59; y-axis) for two crop rows (Row A - green, Row B - blue) across two days. Panel titles report the total number of observations ( $N$ ) for each day and row.

			Human obs.	TP	FP	FN	Human obs.	Software obs.
Row	Date	No. of
videos	Insect detection				Flower visit detection
A	Day 1	3	6	5	2	1	7	3
A	Day 2	3	10	10	1	0	2	2
B	Day 1	3	13	10	1	3	8	8
B	Day 2	3	10	8	0	2	3	8

Table 1: Validation of automated insect and flower visit detection using the Polytrack software. “Row” and “Date” indicate the video samples analysed, and “No. of videos” the number evaluated. Under insect detection, “Human obs.” represents manually counted honeybees, while “TP”, “FP”, and “FN” denote true positives, false positives (including duplicate tracks of a single insect), and false negatives, respectively. Under flower visit detection, “Human obs.” indicates manually recorded insect–flower interactions and “Software obs.” those identified by the software.

Figure 6: Spatiotemporal variation in insect activity across two blueberry rows. Mean number of insect tracks (points $\pm$ 95% CI) recorded along the length of Row A (green, left column) and Row B (blue, right column) during four two-hour time blocks (09:00–17:00) across two sampling days. X-axis = distance from polytunnel end (m), y-axis = number of tracks. Crosses (x) denote raw values. The time period, and total number of tracks per row ( $T$ ), appear above each panel.

Figure 7: Spatiotemporal variation in flower visits across two blueberry rows. Mean number of flower visits (points $\pm$ 95% CI) recorded along the length of Row A (green, left column) and Row B (blue, right column) during four two-hour time blocks (09:00–17:00) across two sampling days. X-axis = distance from the polytunnel end (m), y-axis = number of flower visits. Crosses (x) denote raw values. The time period, and total number of flower visits per row ( $FV$ ), appear above each panel.

Figure 8: Distribution of insect tracks in relation to time of day and microclimate across two sampling days. Number of insect tracks recorded in Row A (green) and Row B (blue) plotted against time of day (hours), temperature ( ∘ C), and relative humidity (%) for Day 1 (top row) and Day 2 (bottom row). Each point represents the number of bee tracks observed during an individual sampling interval. Grey histograms show the total frequency distribution of non-zero insect track observations across each environmental variable. Red dashed vertical lines indicate the minimum and maximum values recorded for each variable during the sampling period. The left y-axis shows the number of insect tracks per observation, and right y-axis (histograms) shows the total number of non-zero insect track observations.

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The decline of natural pollinators has created a major challenge for crop production in controlled indoor agriculture, particularly in vertical farming environments where natural insect pollination is absent. This motivates the development of robotic systems capable of performing precise flower targeting tasks while minimizing physical interference with delicate floral structures. This paper presents an aerial manipulator platform for perception driven flower detection, localization, and approach in vertical farming environments. The proposed system integrates onboard RGBD based perception, model predictive path integral (MPPI) based unmanned aerial vehicle (UAV) control on a PX4 platform, and a lightweight 2DoF manipulator for precise end effector positioning. The platform is evaluated in both MuJoCo simulation and UAV lab experiments using a flower targeting testbed. The experimental results demonstrate stable UAV flight, reliable flower localization, and centimeter level end effector positioning accuracy. In simulation, the proposed controller achieves consistent trajectory convergence and accurate target alignment. In the real world UAV lab environment, the integrated perception control manipulation framework enables stable flower targeted positioning and end effector alignment under constrained aerial operation. These results validate the proposed aerial manipulator as a robust robotic carrier and positioning framework for future contactless pollination systems. While the current study focuses on perception guided targeting and positioning, the developed platform provides a practical foundation for integrating advanced contactless end effectors, including acoustic based pollen manipulation modules, in future work.

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